"""File-based images that have data arrays The class:`DataObjImage` class defines an image that extends the :class:`FileBasedImage` by adding an array-like object, named ``dataobj``. This can either be an actual numpy array, or an object that: * returns an array from ``numpy.asanyarray(obj)``; * has an attribute or property ``shape``. """ from __future__ import annotations import typing as ty import numpy as np from .deprecated import deprecate_with_version from .filebasedimages import FileBasedHeader, FileBasedImage if ty.TYPE_CHECKING: import numpy.typing as npt from ._typing import Self from .arrayproxy import ArrayLike from .fileholders import FileMap from .filename_parser import FileSpec class DataobjImage(FileBasedImage): """Template class for images that have dataobj data stores""" _data_cache: np.ndarray | None _fdata_cache: np.ndarray[ty.Any, np.dtype[np.floating]] | None def __init__( self, dataobj: ArrayLike, header: FileBasedHeader | ty.Mapping | None = None, extra: ty.Mapping | None = None, file_map: FileMap | None = None, ): """Initialize dataobj image The datobj image is a combination of (dataobj, header), with optional metadata in `extra`, and filename / file-like objects contained in the `file_map` mapping. Parameters ---------- dataobj : object Object containing image data. It should be some object that returns an array from ``np.asanyarray``. It should have ``shape`` and ``ndim`` attributes or properties header : None or mapping or header instance, optional metadata for this image format extra : None or mapping, optional metadata to associate with image that cannot be stored in the metadata of this image type file_map : mapping, optional mapping giving file information for this image format """ super().__init__(header=header, extra=extra, file_map=file_map) self._dataobj = dataobj self._data_cache = None self._fdata_cache = None @property def dataobj(self) -> ArrayLike: return self._dataobj @deprecate_with_version( 'get_data() is deprecated in favor of get_fdata(), which has a more predictable return ' 'type. To obtain get_data() behavior going forward, use numpy.asanyarray(img.dataobj).', '3.0', '5.0', ) def get_data(self, caching='fill'): """Return image data from image with any necessary scaling applied .. WARNING:: We recommend you use the ``get_fdata`` method instead of the ``get_data`` method, because it is easier to predict the return data type. ``get_data`` will be deprecated around November 2019 and removed around November 2021. If you don't care about the predictability of the return data type, and you want the minimum possible data size in memory, you can replicate the array that would be returned by ``img.get_data()`` by using ``np.asanyarray(img.dataobj)``. The image ``dataobj`` property can be an array proxy or an array. An array proxy is an object that knows how to load the image data from disk. An image with an array proxy ``dataobj`` is a *proxy image*; an image with an array in ``dataobj`` is an *array image*. The default behavior for ``get_data()`` on a proxy image is to read the data from the proxy, and store in an internal cache. Future calls to ``get_data`` will return the cached array. This is the behavior selected with `caching` == "fill". Once the data has been cached and returned from an array proxy, if you modify the returned array, you will also modify the cached array (because they are the same array). Regardless of the `caching` flag, this is always true of an array image. Parameters ---------- caching : {'fill', 'unchanged'}, optional See the Notes section for a detailed explanation. This argument specifies whether the image object should fill in an internal cached reference to the returned image data array. "fill" specifies that the image should fill an internal cached reference if currently empty. Future calls to ``get_data`` will return this cached reference. You might prefer "fill" to save the image object from having to reload the array data from disk on each call to ``get_data``. "unchanged" means that the image should not fill in the internal cached reference if the cache is currently empty. You might prefer "unchanged" to "fill" if you want to make sure that the call to ``get_data`` does not create an extra (cached) reference to the returned array. In this case it is easier for Python to free the memory from the returned array. Returns ------- data : array array of image data See also -------- uncache: empty the array data cache Notes ----- All images have a property ``dataobj`` that represents the image array data. Images that have been loaded from files usually do not load the array data from file immediately, in order to reduce image load time and memory use. For these images, ``dataobj`` is an *array proxy*; an object that knows how to load the image array data from file. By default (`caching` == "fill"), when you call ``get_data`` on a proxy image, we load the array data from disk, store (cache) an internal reference to this array data, and return the array. The next time you call ``get_data``, you will get the cached reference to the array, so we don't have to load the array data from disk again. Array images have a ``dataobj`` property that already refers to an array in memory, so there is no benefit to caching, and the `caching` keywords have no effect. For proxy images, you may not want to fill the cache after reading the data from disk because the cache will hold onto the array memory until the image object is deleted, or you use the image ``uncache`` method. If you don't want to fill the cache, then always use ``get_data(caching='unchanged')``; in this case ``get_data`` will not fill the cache (store the reference to the array) if the cache is empty (no reference to the array). If the cache is full, "unchanged" leaves the cache full and returns the cached array reference. The cache can affect the behavior of the image, because if the cache is full, or you have an array image, then modifying the returned array will modify the result of future calls to ``get_data()``. For example you might do this: >>> import os >>> import nibabel as nib >>> from nibabel.testing import data_path >>> img_fname = os.path.join(data_path, 'example4d.nii.gz') >>> img = nib.load(img_fname) # This is a proxy image >>> nib.is_proxy(img.dataobj) True The array is not yet cached by a call to "get_data", so: >>> img.in_memory False After we call ``get_data`` using the default `caching` == 'fill', the cache contains a reference to the returned array ``data``: >>> data = img.get_data() >>> img.in_memory True We modify an element in the returned data array: >>> data[0, 0, 0, 0] 0 >>> data[0, 0, 0, 0] = 99 >>> data[0, 0, 0, 0] 99 The next time we call 'get_data', the method returns the cached reference to the (modified) array: >>> data_again = img.get_data() >>> data_again is data True >>> data_again[0, 0, 0, 0] 99 If you had *initially* used `caching` == 'unchanged' then the returned ``data`` array would have been loaded from file, but not cached, and: >>> img = nib.load(img_fname) # a proxy image again >>> data = img.get_data(caching='unchanged') >>> img.in_memory False >>> data[0, 0, 0] = 99 >>> data_again = img.get_data(caching='unchanged') >>> data_again is data False >>> data_again[0, 0, 0, 0] 0 """ if caching not in ('fill', 'unchanged'): raise ValueError('caching value should be "fill" or "unchanged"') if self._data_cache is not None: return self._data_cache data = np.asanyarray(self._dataobj) if caching == 'fill': self._data_cache = data return data def get_fdata( self, caching: ty.Literal['fill', 'unchanged'] = 'fill', dtype: npt.DTypeLike = np.float64, ) -> np.ndarray[ty.Any, np.dtype[np.floating]]: """Return floating point image data with necessary scaling applied The image ``dataobj`` property can be an array proxy or an array. An array proxy is an object that knows how to load the image data from disk. An image with an array proxy ``dataobj`` is a *proxy image*; an image with an array in ``dataobj`` is an *array image*. The default behavior for ``get_fdata()`` on a proxy image is to read the data from the proxy, and store in an internal cache. Future calls to ``get_fdata`` will return the cached array. This is the behavior selected with `caching` == "fill". Once the data has been cached and returned from an array proxy, if you modify the returned array, you will also modify the cached array (because they are the same array). Regardless of the `caching` flag, this is always true of an array image. Parameters ---------- caching : {'fill', 'unchanged'}, optional See the Notes section for a detailed explanation. This argument specifies whether the image object should fill in an internal cached reference to the returned image data array. "fill" specifies that the image should fill an internal cached reference if currently empty. Future calls to ``get_fdata`` will return this cached reference. You might prefer "fill" to save the image object from having to reload the array data from disk on each call to ``get_fdata``. "unchanged" means that the image should not fill in the internal cached reference if the cache is currently empty. You might prefer "unchanged" to "fill" if you want to make sure that the call to ``get_fdata`` does not create an extra (cached) reference to the returned array. In this case it is easier for Python to free the memory from the returned array. dtype : numpy dtype specifier A numpy dtype specifier specifying a floating point type. Data is returned as this floating point type. Default is ``np.float64``. Returns ------- fdata : array Array of image data of data type `dtype`. See also -------- uncache: empty the array data cache Notes ----- All images have a property ``dataobj`` that represents the image array data. Images that have been loaded from files usually do not load the array data from file immediately, in order to reduce image load time and memory use. For these images, ``dataobj`` is an *array proxy*; an object that knows how to load the image array data from file. By default (`caching` == "fill"), when you call ``get_fdata`` on a proxy image, we load the array data from disk, store (cache) an internal reference to this array data, and return the array. The next time you call ``get_fdata``, you will get the cached reference to the array, so we don't have to load the array data from disk again. Array images have a ``dataobj`` property that already refers to an array in memory, so there is no benefit to caching, and the `caching` keywords have no effect. For proxy images, you may not want to fill the cache after reading the data from disk because the cache will hold onto the array memory until the image object is deleted, or you use the image ``uncache`` method. If you don't want to fill the cache, then always use ``get_fdata(caching='unchanged')``; in this case ``get_fdata`` will not fill the cache (store the reference to the array) if the cache is empty (no reference to the array). If the cache is full, "unchanged" leaves the cache full and returns the cached array reference. The cache can effect the behavior of the image, because if the cache is full, or you have an array image, then modifying the returned array will modify the result of future calls to ``get_fdata()``. For example you might do this: >>> import os >>> import nibabel as nib >>> from nibabel.testing import data_path >>> img_fname = os.path.join(data_path, 'example4d.nii.gz') >>> img = nib.load(img_fname) # This is a proxy image >>> nib.is_proxy(img.dataobj) True The array is not yet cached by a call to "get_fdata", so: >>> img.in_memory False After we call ``get_fdata`` using the default `caching` == 'fill', the cache contains a reference to the returned array ``data``: >>> data = img.get_fdata() >>> img.in_memory True We modify an element in the returned data array: >>> data[0, 0, 0, 0] 0.0 >>> data[0, 0, 0, 0] = 99 >>> data[0, 0, 0, 0] 99.0 The next time we call 'get_fdata', the method returns the cached reference to the (modified) array: >>> data_again = img.get_fdata() >>> data_again is data True >>> data_again[0, 0, 0, 0] 99.0 If you had *initially* used `caching` == 'unchanged' then the returned ``data`` array would have been loaded from file, but not cached, and: >>> img = nib.load(img_fname) # a proxy image again >>> data = img.get_fdata(caching='unchanged') >>> img.in_memory False >>> data[0, 0, 0] = 99 >>> data_again = img.get_fdata(caching='unchanged') >>> data_again is data False >>> data_again[0, 0, 0, 0] 0.0 """ if caching not in ('fill', 'unchanged'): raise ValueError('caching value should be "fill" or "unchanged"') dtype = np.dtype(dtype) if not issubclass(dtype.type, np.inexact): raise ValueError(f'{dtype} should be floating point type') # Return cache if cache present and of correct dtype. if self._fdata_cache is not None: if self._fdata_cache.dtype.type == dtype.type: return self._fdata_cache # Always return requested data type # For array proxies, will attempt to confine data array to dtype # during scaling data = np.asanyarray(self._dataobj, dtype=dtype) if caching == 'fill': self._fdata_cache = data return data @property def in_memory(self) -> bool: """True when any array data is in memory cache There are separate caches for `get_data` reads and `get_fdata` reads. This property is True if either of those caches are set. """ return ( isinstance(self._dataobj, np.ndarray) or self._fdata_cache is not None or self._data_cache is not None ) def uncache(self) -> None: """Delete any cached read of data from proxied data Remember there are two types of images: * *array images* where the data ``img.dataobj`` is an array * *proxy images* where the data ``img.dataobj`` is a proxy object If you call ``img.get_fdata()`` on a proxy image, the result of reading from the proxy gets cached inside the image object, and this cache is what gets returned from the next call to ``img.get_fdata()``. If you modify the returned data, as in:: data = img.get_fdata() data[:] = 42 then the next call to ``img.get_fdata()`` returns the modified array, whether the image is an array image or a proxy image:: assert np.all(img.get_fdata() == 42) When you uncache an array image, this has no effect on the return of ``img.get_fdata()``, but when you uncache a proxy image, the result of ``img.get_fdata()`` returns to its original value. """ self._fdata_cache = None self._data_cache = None @property def shape(self) -> tuple[int, ...]: return self._dataobj.shape @property def ndim(self) -> int: return self._dataobj.ndim @classmethod def from_file_map( klass, file_map: FileMap, *, mmap: bool | ty.Literal['c', 'r'] = True, keep_file_open: bool | None = None, ) -> Self: """Class method to create image from mapping in ``file_map`` Parameters ---------- file_map : dict Mapping with (key, value) pairs of (``file_type``, FileHolder instance giving file-likes for each file needed for this image type. mmap : {True, False, 'c', 'r'}, optional, keyword only `mmap` controls the use of numpy memory mapping for reading image array data. If False, do not try numpy ``memmap`` for data array. If one of {'c', 'r'}, try numpy memmap with ``mode=mmap``. A `mmap` value of True gives the same behavior as ``mmap='c'``. If image data file cannot be memory-mapped, ignore `mmap` value and read array from file. keep_file_open : { None, True, False }, optional, keyword only `keep_file_open` controls whether a new file handle is created every time the image is accessed, or a single file handle is created and used for the lifetime of this ``ArrayProxy``. If ``True``, a single file handle is created and used. If ``False``, a new file handle is created every time the image is accessed. If ``file_map`` refers to an open file handle, this setting has no effect. The default value (``None``) will result in the value of ``nibabel.arrayproxy.KEEP_FILE_OPEN_DEFAULT`` being used. Returns ------- img : DataobjImage instance """ raise NotImplementedError @classmethod def from_filename( klass, filename: FileSpec, *, mmap: bool | ty.Literal['c', 'r'] = True, keep_file_open: bool | None = None, ) -> Self: """Class method to create image from filename `filename` Parameters ---------- filename : str Filename of image to load mmap : {True, False, 'c', 'r'}, optional, keyword only `mmap` controls the use of numpy memory mapping for reading image array data. If False, do not try numpy ``memmap`` for data array. If one of {'c', 'r'}, try numpy memmap with ``mode=mmap``. A `mmap` value of True gives the same behavior as ``mmap='c'``. If image data file cannot be memory-mapped, ignore `mmap` value and read array from file. keep_file_open : { None, True, False }, optional, keyword only `keep_file_open` controls whether a new file handle is created every time the image is accessed, or a single file handle is created and used for the lifetime of this ``ArrayProxy``. If ``True``, a single file handle is created and used. If ``False``, a new file handle is created every time the image is accessed. The default value (``None``) will result in the value of ``nibabel.arrayproxy.KEEP_FILE_OPEN_DEFAULT`` being used. Returns ------- img : DataobjImage instance """ if mmap not in (True, False, 'c', 'r'): raise ValueError("mmap should be one of {True, False, 'c', 'r'}") file_map = klass.filespec_to_file_map(filename) return klass.from_file_map(file_map, mmap=mmap, keep_file_open=keep_file_open) load = from_filename